AB0002 THE GENETIC BACKGROUND OF ANKYLOSING SPONDYLITIS INDICATES A STRONG CORRELATION WITH OTHER AUTOIMMUNE DISEASES
Authors: L. Papageorgiou, M. Zervou, T. Zormpa, R. Golfinopoulou, D. Vlachakis, G. Bertsias, G. Goulielmos, E. Eliopoulos
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Ankylosing Spondylitis (AS) is considered to be a rare autoimmune disease that has been studied in genetic level the last decades [1]. The cases reported suggest that there might be an improvement in disease diagnosis with an overlap by other autoimmune diseases [2]. Patients can be diagnosed and the clinical symptoms are in alignment with a diagnosis of the disease. Meanwhile, novel studies propose various genetic factors that may be related to the disease has emerged. Most of the patients carrying various subtypes of the <i>HLA-B27</i> gene, such as B*2702, B*2703 and B*2710, are at an increased risk of developing AS [3]. Importantly, the relationship between the genetic factors and the development of the disease is still remains unclear. <h3>Objectives</h3> To create the disease genomic grammar (DGG) with the most credible genetic and epigenetic variants as well as and single nucleotide polymorphisms (SNPs) causing the basis for the development of AS and identify the correlation with other candidate autoimmune diseases [4]. <h3>Methods</h3> AS related publications have been analyzed with the use of data mining and semantic techniques for the extraction of information regarding SNPs associated with the disease and the estimation of a candidate DGG. The extracted SNPs have been filtered, evaluated, annotated, and classified in the DGG, using several layers of information from well-known biological databases including dbSNP, LitVar, ClinVar, OMIM and KEGG. Moreover, the AS DGG has been further analyzed towards identifying the hidden connections with other autoimmune diseases. <h3>Results</h3> A holistic genetic map of the AS DGG with 658 related SNPs has been estimated and the specific sub-clusters with crucial SNPs have been correlated with several other autoimmune disease including RA, PS, CD and SEL (Figure 1). This indicates that AS has a common genetic background as many other autoimmune diseases [5]. The candidate DGG of the AS has been estimated using the SNPs annotated information and 3 major clusters of SNPs have been estimated. The DGG of the AS, 85% responds to non-coding regions and 15% responds to coding regions of which 684 related with promoter and coding gene regions as well as pseudogenes, 74 with LNC, miR and LOC regions and 54 with uncharacterized regions (Figure 1). Moreover, they have been identified the most highly correlated genes with AS using the DGG and the frequency-corresponding SNPs (Table 1). <h3>Conclusion</h3> Despite the several studies performed in AS, is still difficult to identify and categorize the representative variants that may contribute to its. Therefore, crucial genes and specific epigenetic locations must be recognized towards understanding the biological mechanisms of the disease [6, 7]. In the framework of the present study, the DGG of AS was created, using novel bioinformatic techniques. The identification of the DGG in AS may assist in the deeper understanding of this rare disease and the hidden connections with other autoimmune diseases, and will also provide beneficial knowledge regarding common biological pathways leading to autoimmunity regardless of the clinical phenotype of each disease. <h3>References</h3> [1]Dakwar, E., et al. Neurosurg Focus, 2008. <b>24</b>(1): p. E2. [2]Goulielmos, G.N. and M.I. Zervou. J Rheumatol, 2021. <b>48</b>(9): p. 1492-1493. [3]Bowness, P. Annu Rev Immunol, 2015. <b>33</b>: p. 29-48. [4]Papageorgiou, L., et al. Annals Rheum. Dis., 2022. <b>81</b> (Suppl 1): p. 70-71. [5]Wang, Y., et al. Ann Rheum Dis, 2021. [6]Papageorgiou, L., et al. Int J Mol Med, 2021. <b>47</b>(6). [7]Papageorgiou, L., et al. Int J Mol Med, 2022. <b>49</b>(1). <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.